Generative AI · Evidence · Courts

The GPTJudge: Justice in a Generative AI World

Maura R. Grossman, Paul W. Grimm, Daniel G. Brown, and Molly (Yiming) Xu

Duke Law & Technology Review 23, 1–34 (2023)

A practical account of how generative AI changes questions of authenticity, admissibility, litigation practice, and judicial decision making.

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Overview

Generative AI can produce text, images, audio, and video that may be difficult to distinguish from human-created or authentic evidence. This article explains the technology in accessible terms and considers what happens when AI-generated material is offered in court—or when genuine evidence is challenged as a possible deepfake.

The authors examine authentication under existing evidence rules, the likely role of forensic experts, risks of increased litigation cost, effects on juries, and the possibility that generative AI may alter both substantive law and the way lawyers litigate and judges decide cases.

Why this paper matters. Rather than waiting for new rules tailored to every technical development, the article shows how courts and counsel can address generative-AI evidence using existing evidentiary principles, supported by practical, step-by-step procedures.

Key contributions

Explains generative AI and deepfakes for judges and lawyers.
Distinguishes evidence known to be AI-generated from evidence merely alleged to be fake.
Applies existing rules of relevance, authenticity, and admissibility to GenAI output.
Offers practical guidance for preserving, challenging, and evaluating disputed evidence.
Considers broader implications for litigation, intellectual-property law, and judicial work.

Suggested citation

Maura R. Grossman, Paul W. Grimm, Daniel G. Brown & Molly (Yiming) Xu, The GPTJudge: Justice in a Generative AI World, 23 Duke Law & Technology Review 1–34 (2023).